Extraction method for parasitic capacitances and inductances of HEMT models

Zhang, Hengshuang; Ma, Pengtao; Lu, Yang; Zhao, Bingxuan; Zheng, Jiaxin; Ma, Xiaojuan

doi:10.1016/j.sse.2016.12.003

Cited by 12 publications

(8 citation statements)

References 11 publications

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“…The network‐based extraction techniques are still challenging and need for further improvements due to the diversity of process technologies. By using open/short/through test structures, the parasitic capacitances and inductances can be determined directly . The intrinsic elements can be extracted after de‐embedding the parasitic.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

RF modeling and parameter extraction for GaAs‐based on‐chip inductors

Gao

Zhang

Jin

2020

Micro & Optical Tech Letters

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RF modeling and equivalent circuit model parameter extraction for GaAs‐based on‐chip inductors are presented in this article. An improved model that takes into account of the substrate loss effects based on the S parameters on wafer measurement is proposed. The parameter extraction method combines the analytical expressions and the empirical optimization procedure, which does not require any additional test structures. The intrinsic elements determined by utilizing the analytical method are described as functions of the parasitic elements. After that, an advanced design system is used to optimize the parasitic parameters with very small dispersion of initial values. Good agreement is obtained between the simulated and measured data up to 50 GHz.

show abstract

Section: Introductionmentioning

confidence: 99%

“…By using open/short/through test structures, the parasitic capacitances and inductances can be determined directly. [4][5][6][7][8] The intrinsic elements can be extracted after de-embedding the parasitic. The test structure methods need additional open/short/through structures for each device size, and more chip area is needed.…”

Section: Introductionmentioning

confidence: 99%

RF modeling and parameter extraction for GaAs‐based on‐chip inductors

Gao

Zhang

Jin

2020

Micro & Optical Tech Letters

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“…14,15 Furthermore, many of the reported approaches addressed the extraction methodology based on de-embedding structures, cold FET condition, and neural network to model GaN devices. [16][17][18][19][20][21][22][23][24] In these reports, one of the extraction procedures was based on a two-step optimization process. [16][17][18][19][20][21] In such techniques, first the starting values were found either from measurements or using genetic algorithm, and then simplex algorithm technique was employed to find the optimal values for each element.…”

Section: Introductionmentioning

confidence: 99%

“…For example, iterative procedures that considered the effect of channel capacitance provided very good accuracy at a wide range of bias conditions . Furthermore, many of the reported approaches addressed the extraction methodology based on de‐embedding structures, cold FET condition, and neural network to model GaN devices . In these reports, one of the extraction procedures was based on a two‐step optimization process .…”

Section: Introductionmentioning

confidence: 99%

An accurate and simplified small signal parameter extraction method for GaN HEMT

Khusro

Hashmi

Ansari

et al. 2019

Circuit Theory & Apps

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Summary In this paper, development of a small signal model for 2 × 200 μm GaN HEMT based on the conventional 20‐element model is presented. The proposed model presents a direct parameter extraction algorithm, instead of the hybrid optimization approach, that provides simplification, accuracy, and less computational complexity. The extrinsic elements are extracted using a modified cold pinch‐off condition while discarding the unwanted forward biasing of the gate. The negative drain to source capacitance Cds is also observed in the ohmic region (for smaller VDS). An excellent agreement found between the measured and modeled data for a wide range of frequencies and bias values shows the effectiveness of the proposed approach. The proposed modeling technique is validated with a good agreement between the achieved bias dependency of intrinsic parameter values and the respective theoretical parameter values.

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“…An increasing number of publications are testament to the appeal of machine learning in device modelling. It is, therefore, an emerging and appropriate alternate technique to the conventional equivalent circuit-based modelling methods [5][6][7][8][9][10][11][12][13][14] that can be used for modelling the microwave devices. It essentially aids in modelling the devices based on data approximation.…”

Section: Introductionmentioning

confidence: 99%

Enabling the development of accurate intrinsic parameter extraction model for GaN HEMT using support vector regression (SVR)

Khusro¹,

Hashmi²,

Ansari³

2019

IET Microwaves, Antennas & Propagation

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This study employs support vector regression (SVR) to develop an accurate and reliable intrinsic parameter extraction model for gallium nitride (GaN) high electron mobility transistors (HEMT) using two different geometries of 2 × 200 μm and 4 × 100 µm. The key aspect of the proposed approach is the use of nonlinear Gaussian kernel to transform the input space into a high‐dimensional feature space. It then allows the application of learning technique to develop a reliable procedure for parameter extraction. The proposed extraction model of GaN HEMT has been developed for a broad range of frequency, from 1 to 18 GHz, with multi‐biasing sets for HEMTs of two different geometries. Moreover, the proposed model is made scalable in terms of geometry parameters and therefore can be used to predict the intrinsic parameters and enumerate scaling efficiency of GaN HEMTs by investigating the geometry parameters. A good agreement is observed between the measured S‐parameters and the proposed model for the complete frequency range. It is shown that the proposed approach is simple, novel and can be readily incorporated into computer‐aided design tool for an accurate and expedited design process of RF and microwave circuits.

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Extraction method for parasitic capacitances and inductances of HEMT models

Cited by 12 publications

References 11 publications

RF modeling and parameter extraction for GaAs‐based on‐chip inductors

RF modeling and parameter extraction for GaAs‐based on‐chip inductors

An accurate and simplified small signal parameter extraction method for GaN HEMT

Enabling the development of accurate intrinsic parameter extraction model for GaN HEMT using support vector regression (SVR)

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